发表机构
University of Glasgow（格拉斯哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究训练AlexNet等四种深度学习模型自动分析结直肠癌患者CT衍生身体成分，GoogLeNet和AlexNet表现优异，模型可部署于网络应用，有望简化临床身体成分分析流程。
AI 中文摘要
背景：利用计算机断层扫描（CT）扫描进行准确的身体成分分析，对评估骨骼肌面积（SMA）和骨骼肌密度（SMD）至关重要，这两项是癌症患者营养状况的关键标志物。传统手动方法劳动强度大，且需要专业知识，限制了其常规临床应用。因此，本研究作为一项可行性和试点调查，探索临床工作流程中基于深度学习的自动回归在身体成分分析中的潜力。方法：训练四种深度学习架构（AlexNet、UNet、GoogLeNet和ResNet34），从结直肠癌患者的CT扫描中预测SMA、SMD、皮下脂肪面积（SFA）和内脏脂肪面积（VFA）。系统的超参数优化确定了最准确的模型，随后将这些模型部署到网络应用中供临床使用。结果：GoogLeNet表现最佳，SMA预测的平均百分比误差（PE）为4.96%，而AlexNet在SMD预测中达到8.12%。独立测试显示出稳健的准确性，80%的病例能正确分类身体成分指标。该网络应用提供快速且一致的输出，支持集成到临床工作流程中。结论：优化后的深度学习模型，尤其是GoogLeNet和AlexNet，可实现CT衍生身体成分分析的自动化，SMA的平均百分比误差（PE）为4.96%，SMD为8.12%。这些工具有望通过减少手动分割所需的时间和专业知识来简化临床实践，有必要在更大、更多样化的数据集上进行进一步验证。
英文摘要
Background: Accurate body composition analysis using Computed Tomography (CT) scans is essential for assessing skeletal muscle area (SMA) and skeletal muscle density (SMD), key markers of nutritional status in cancer patients. Conventional manual methods are labour-intensive and require specialist expertise, limiting their routine clinical use. Therefore, this study serves as a feasibility and pilot investigation to explore the potential of deep learning-based automated regression for body composition analysis within a clinical workflow. Methods: Four deep learning architectures (AlexNet, UNet, GoogLeNet, and ResNet34) were trained to predict SMA, SMD, subcutaneous fat area (SFA), and visceral fat area (VFA) from CT scans of colorectal cancer patients. Systematic hyperparameter optimization identified the most accurate models, which were subsequently implemented in a web application for clinical use. Results: GoogLeNet achieved the best performance, with a mean percentage error (PE) of 4.96% for SMA prediction, while AlexNet reached 8.12% for SMD. Independent testing demonstrated robust accuracy, correctly classifying body composition metrics in 80% of cases. The web application delivered rapid and consistent outputs, supporting integration into clinical workflows. Conclusion: Optimized deep learning models, particularly GoogLeNet and AlexNet, can automate CT-derived body composition analysis with a Mean Percentage Error (PE) of 4.96% for SMA and 8.12% for SMD. These tools have the potential to streamline clinical practice by reducing the time and expertise required for manual segmentation. Further validation in larger, more diverse datasets is warranted.
Comments27 pages, 4 figures
